best: score 0.5541 (recall 0.5759, ndcg 0.4670)

- DENSE_PREFETCH_K 80 → 120
- RERANK_LIMIT 25 → 35
- add question.text as extra dense when differs from search_text

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

doc/curl_api_test.md

@@ -0,0 +1,366 @@
+# Curl API Test
+
+## Sources
+
+- Canonical contracts: `doc/ТЗ_на_хакатон_Индексация_и_поиск_по_сообщениям.pdf`
+- Runnable examples and local launch notes: `README.md`
+- Actual local wiring: `docker-compose.yml`
+
+PDF gives the strict request/response schemas for `POST /index`, `POST /sparse_embedding`, and `POST /search`.
+`README.md` adds ready curl examples for the minimal requests.
+This file normalizes both into checks against the current local compose stack.
+
+## Compose Wiring
+
+- `index`: `http://localhost:8001`
+- `search`: `http://localhost:8002`
+- `qdrant`: `http://localhost:6334`
+- Inside compose, services use `QDRANT_URL=http://qdrant:6333`
+- Collection name from `.env`: `evaluation`
+- Vector names from `.env`: `dense` and `sparse`
+
+Note: current `docker-compose.yml` publishes Qdrant as `6334:6333`, while `README.md` still says `localhost:6333`. For local checks in this repo state, use `localhost:6334`.
+
+## Extracted API Requests
+
+### `GET /health`
+
+Both services must answer `200 OK`.
+
+```bash
+curl -sS http://localhost:8001/health
+curl -sS http://localhost:8002/health
+```
+
+Expected shape:
+
+```json
+{"status":"ok"}
+```
+
+### `POST /index`
+
+Schema from the PDF:
+
+- body root: `data`
+- `data.chat`
+- `data.overlap_messages[]`
+- `data.new_messages[]`
+
+Runnable request:
+
+```bash
+curl -sS -X POST http://localhost:8001/index \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "data": {
+      "chat": {
+        "id": "chat-1",
+        "name": "Go Nova",
+        "sn": "chat-1@chat.agent",
+        "type": "channel",
+        "is_public": true
+      },
+      "overlap_messages": [
+        {
+          "id": "1",
+          "time": 1710000000,
+          "text": "Обсуждаем релиз Go",
+          "sender_id": "u1",
+          "file_snippets": "",
+          "parts": [],
+          "mentions": [],
+          "member_event": null,
+          "is_system": false,
+          "is_hidden": false,
+          "is_forward": false,
+          "is_quote": false
+        }
+      ],
+      "new_messages": [
+        {
+          "id": "2",
+          "time": 1710000060,
+          "text": "Релиз Go перенесли на следующую неделю",
+          "sender_id": "u2",
+          "file_snippets": "",
+          "parts": [],
+          "mentions": [],
+          "member_event": null,
+          "is_system": false,
+          "is_hidden": false,
+          "is_forward": false,
+          "is_quote": false
+        }
+      ]
+    }
+  }'
+```
+
+Observed response:
+
+```json
+{
+  "results": [
+    {
+      "page_content": "u1: Обсуждаем релиз Go\nu2: Релиз Go перенесли на следующую неделю",
+      "dense_content": "[2024-03-09 16:00] sender:u1\nОбсуждаем релиз Go\n[2024-03-09 16:01] sender:u2\nРелиз Go перенесли на следующую неделю",
+      "sparse_content": "u1 Обсуждаем релиз Go u2 Релиз Go перенесли на следующую неделю",
+      "message_ids": ["2"]
+    }
+  ]
+}
+```
+
+Note: overlap messages are used as context, but are not included in returned `message_ids`.
+
+### `POST /sparse_embedding`
+
+Schema from the PDF:
+
+- body root: `texts: string[]`
+
+Runnable request:
+
+```bash
+curl -sS -X POST http://localhost:8001/sparse_embedding \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "texts": [
+      "Релиз Go перенесли на следующую неделю",
+      "VK GPT обсуждали в отдельном чате"
+    ]
+  }'
+```
+
+Observed response:
+
+```json
+{
+  "vectors": [
+    {
+      "indices": [275068001, 108710752, 842257583, 1159207840, 2129888840, 703082301],
+      "values": [1.6652868125369606, 1.6652868125369606, 1.6652868125369606, 1.6652868125369606, 1.6652868125369606, 1.6652868125369606]
+    },
+    {
+      "indices": [73209461, 751565418, 59863655, 1856729543, 2036701913, 1943620510],
+      "values": [1.6652868125369606, 1.6652868125369606, 1.6652868125369606, 1.6652868125369606, 1.6652868125369606, 1.6652868125369606]
+    }
+  ]
+}
+```
+
+### `POST /search`
+
+Minimal request from `README.md`:
+
+```bash
+curl -sS -X POST http://localhost:8002/search \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "question": {
+      "text": "Что писали про релиз Go?"
+    }
+  }'
+```
+
+Full schema from the PDF:
+
+```json
+{
+  "question": {
+    "text": "Что писали про релиз Go?",
+    "asker": "u2",
+    "asked_on": "2024-03-09",
+    "variants": ["релиз go перенесли?", "обсуждение релиза go"],
+    "hyde": ["В чате пишут, что релиз Go перенесли на следующую неделю."],
+    "keywords": ["релиз", "Go", "перенесли"],
+    "entities": {
+      "people": ["u2"],
+      "emails": [],
+      "documents": [],
+      "names": ["Go"],
+      "links": []
+    },
+    "date_mentions": ["следующая неделя", "2024-03-09"],
+    "date_range": {
+      "from": "2024-03-09T00:00:00Z",
+      "to": "2024-03-10T00:00:00Z"
+    },
+    "search_text": "релиз Go перенесли на следующую неделю"
+  }
+}
+```
+
+## Checks Run
+
+### 1. Health checks
+
+Commands:
+
+```bash
+curl -sS http://localhost:8001/health
+curl -sS http://localhost:8002/health
+```
+
+Observed:
+
+```json
+{"status":"ok"}
+{"status":"ok"}
+```
+
+### 2. Qdrant collection exists, but starts empty
+
+Command:
+
+```bash
+curl -sS http://localhost:6334/collections/evaluation
+```
+
+Observed before manual insert:
+
+- `points_count: 0`
+- `indexed_vectors_count: 0`
+
+This matches the README note that local compose creates the collection, but the template flow does not automatically upsert `/index` output into Qdrant.
+
+### 3. `/index` works
+
+Observed:
+
+- HTTP request completed successfully
+- service returned one chunk
+- returned fields match the contract: `page_content`, `dense_content`, `sparse_content`, `message_ids`
+
+### 4. `/sparse_embedding` works
+
+Observed:
+
+- HTTP request completed successfully
+- response returned `vectors[]`
+- each vector contains `indices[]` and `values[]`
+
+### 5. `/search` on an empty collection returns an empty result
+
+Command:
+
+```bash
+curl -sS -X POST http://localhost:8002/search \
+  -H 'Content-Type: application/json' \
+  -d '{"question":{"text":"Что писали про релиз Go?"}}'
+```
+
+Observed:
+
+```json
+{"results":[]}
+```
+
+This is expected while `evaluation` has no points.
+
+### 6. Manual Qdrant upsert for end-to-end smoke test
+
+To verify `/search` end-to-end, I inserted one synthetic point into local Qdrant with:
+
+- point id `1001`
+- dummy dense vector of size `1024`
+- sparse vector under field `sparse`
+- payload containing `page_content` and `metadata.message_ids=["2"]`
+
+Command:
+
+```bash
+vec=$(awk 'BEGIN{for(i=0;i<1024;i++) printf "%s%d", (i?",":""), (i==0)}')
+curl -sS -X PUT 'http://localhost:6334/collections/evaluation/points?wait=true' \
+  -H 'Content-Type: application/json' \
+  -d "{\"points\":[{\"id\":1001,\"vector\":{\"dense\":[${vec}],\"sparse\":{\"indices\":[1],\"values\":[1.0]}},\"payload\":{\"page_content\":\"u1: Обсуждаем релиз Go\\nu2: Релиз Go перенесли на следующую неделю\",\"metadata\":{\"message_ids\":[\"2\"],\"participants\":[\"u1\",\"u2\"],\"start\":\"2024-03-09T16:00:00Z\",\"end\":\"2024-03-09T16:01:00Z\",\"chat_id\":\"chat-1\",\"chat_name\":\"Go Nova\",\"chat_type\":\"channel\",\"chat_sn\":\"chat-1@chat.agent\"}}}]}"
+```
+
+Observed:
+
+```json
+{"result":{"operation_id":0,"status":"completed"},"status":"ok","time":0.008234969}
+```
+
+Collection state after insert:
+
+- `points_count: 1`
+- `indexed_vectors_count: 1`
+
+### 7. `/search` works after one point is present
+
+Minimal request:
+
+```bash
+curl -sS -X POST http://localhost:8002/search \
+  -H 'Content-Type: application/json' \
+  -d '{"question":{"text":"Что писали про релиз Go?"}}'
+```
+
+Observed:
+
+```json
+{"results":[{"message_ids":["2"]}]}
+```
+
+Enriched request without `date_range`:
+
+```bash
+curl -sS -X POST http://localhost:8002/search \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "question": {
+      "text": "Что писали про релиз Go?",
+      "asker": "u2",
+      "asked_on": "2024-03-09",
+      "variants": ["релиз go перенесли?", "обсуждение релиза go"],
+      "hyde": ["В чате пишут, что релиз Go перенесли на следующую неделю."],
+      "keywords": ["релиз", "Go", "перенесли"],
+      "entities": {
+        "people": ["u2"],
+        "emails": [],
+        "documents": [],
+        "names": ["Go"],
+        "links": []
+      },
+      "date_mentions": ["следующая неделя", "2024-03-09"],
+      "search_text": "релиз Go перенесли на следующую неделю"
+    }
+  }'
+```
+
+Observed:
+
+```json
+{"results":[{"message_ids":["2"]}]}
+```
+
+### 8. Defect: `date_range` request currently fails
+
+The full PDF-shaped request with ISO timestamps in `question.date_range` does not work in the current implementation.
+
+Observed:
+
+```json
+{
+  "detail": "2 validation errors for Range\ngte\n  Input should be a valid number, unable to parse string as a number [type=float_parsing, input_value='2024-03-09T00:00:00Z', input_type=str]\n    For further information visit https://errors.pydantic.dev/2.12/v/float_parsing\nlte\n  Input should be a valid number, unable to parse string as a number [type=float_parsing, input_value='2024-03-10T00:00:00Z', input_type=str]\n    For further information visit https://errors.pydantic.dev/2.12/v/float_parsing"
+}
+```
+
+Interpretation:
+
+- the public request schema accepts ISO date strings
+- current `search` code tries to pass them into a numeric `qdrant_client.models.Range`
+- so `date_range` is a real runtime bug in the current local build
+
+## Bottom Line
+
+- `index /health`: OK
+- `search /health`: OK
+- `POST /index`: OK
+- `POST /sparse_embedding`: OK
+- `POST /search` on empty collection: OK, returns empty list
+- `POST /search` after one test point is inserted: OK
+- `POST /search` with enriched request excluding `date_range`: OK
+- `POST /search` with `date_range` from the PDF schema: FAILS in current implementation

docker-compose.yml

@@ -2,7 +2,7 @@ services:
   qdrant:
     image: qdrant/qdrant:v1.14.1
     ports:
-      - "6333:6333"
+      - "6334:6333"
 
   qdrant-init:
     image: curlimages/curl:8.12.1

index/Dockerfile

@@ -9,7 +9,6 @@ COPY main.py .
 
 ENV HOST=0.0.0.0
 ENV PORT=8000
-ENV CHUNK_SIZE=10
 ENV FASTEMBED_CACHE_PATH=/models/fastembed
 ENV HF_HOME=/models/huggingface
 

index/Makefile

@@ -1,24 +1,26 @@
 LOGIN ?=
 PASSWORD ?=
-TEAM_ID ?= 
+TEAM_ID ?= 35230
 DOCKER_REGISTRY_URL ?= 83.166.249.64:5000
 PORT ?= 8000
 
 IMAGE = $(DOCKER_REGISTRY_URL)/$(TEAM_ID)/index-service:latest
 
-.PHONY: login build run push
+.PHONY: login build run push release
 
 login:
-	@: $(if $(LOGIN),,$(error LOGIN is required for make login))
+	@: $(if $(LOGIN),,$(error LOGIN is required))
-	@: $(if $(PASSWORD),,$(error PASSWORD is required for make login))
+	@: $(if $(PASSWORD),,$(error PASSWORD is required))
-	docker login $(DOCKER_REGISTRY_URL) -u $(LOGIN)  -p $(PASSWORD)
+	docker login $(DOCKER_REGISTRY_URL) -u $(LOGIN) -p $(PASSWORD)
 
 build:
-	@: $(if $(TEAM_ID),,$(error TEAM_ID is required for make build))
+	docker build --platform linux/amd64 -t $(IMAGE) ./
-	docker build -t $(IMAGE) ./
 
 run: build
 	docker run --rm -p $(PORT):8000 $(IMAGE)
 
-push: login build
+push:
 	docker push $(IMAGE)
+
+release: build push
+	@echo "index-service pushed → $(IMAGE)"

index/chunking.py

@@ -1,17 +1,17 @@
 """Message-based chunking with window by count, length, and time gap."""
 
-from .cleaning import CleanedMessage, clean_message
+from cleaning import CleanedMessage, clean_message
-from .rendering import render_dense_content, render_page_content, render_sparse_content
+from rendering import render_dense_content, render_page_content, render_sparse_content
-from .schemas import IndexAPIItem, Message
+from index_schemas import IndexAPIItem, Message
 
-WINDOW_MAX_MESSAGES = 10
+WINDOW_MAX_MESSAGES = 5
-WINDOW_MAX_CHARS = 2048
+WINDOW_MAX_CHARS = 512
 TIME_GAP_SECONDS = 3600
-OVERLAP_MESSAGES = 3
+OVERLAP_MESSAGES = 2
 
 
 def _clean_all(messages: list[Message]) -> list[CleanedMessage]:
-    cleaned = [clean_message(m) for m in messages]
+    cleaned = [clean_message(m) for m in messages if not m.is_system and not m.is_hidden]
     return [c for c in cleaned if not c.is_empty]
 
 

index/main.py

@@ -1,24 +1,192 @@
 import asyncio
 import logging
 import os
+from functools import lru_cache
 from typing import Any
 
 from fastapi import FastAPI, Request
 from fastapi.exceptions import RequestValidationError
 from fastapi.responses import JSONResponse
-
+from pydantic import BaseModel
-from .chunking import build_chunks
-from .schemas import IndexAPIRequest, IndexAPIResponse, SparseEmbeddingRequest
-from .sparse import embed_sparse_texts
 
 HOST = os.getenv("HOST", "0.0.0.0")
-PORT = int(os.getenv("PORT", "8004"))
+PORT = int(os.getenv("PORT", "8000"))
 UVICORN_WORKERS = 8
 
 logging.basicConfig(level=os.getenv("LOG_LEVEL", "INFO"))
 logger = logging.getLogger("index-service")
 
-app = FastAPI(title="Index Service", version="0.2.0")
+
+class Chat(BaseModel):
+    id: str
+    name: str
+    sn: str
+    type: str
+    is_public: bool | None = None
+    members_count: int | None = None
+    members: list[dict[str, Any]] | None = None
+
+
+class Message(BaseModel):
+    id: str
+    thread_sn: str | None = None
+    time: int
+    text: str
+    sender_id: str
+    file_snippets: str
+    parts: list[dict[str, Any]] | None = None
+    mentions: list[str] | None = None
+    member_event: dict[str, Any] | None = None
+    is_system: bool
+    is_hidden: bool
+    is_forward: bool
+    is_quote: bool
+
+
+class ChatData(BaseModel):
+    chat: Chat
+    overlap_messages: list[Message]
+    new_messages: list[Message]
+
+
+class IndexAPIRequest(BaseModel):
+    data: ChatData
+
+
+class IndexAPIItem(BaseModel):
+    page_content: str
+    dense_content: str
+    sparse_content: str
+    message_ids: list[str]
+
+
+class IndexAPIResponse(BaseModel):
+    results: list[IndexAPIItem]
+
+
+class SparseEmbeddingRequest(BaseModel):
+    texts: list[str]
+
+
+class SparseVector(BaseModel):
+    indices: list[int]
+    values: list[float]
+
+
+CHUNK_SIZE = 256
+OVERLAP_SIZE = 128
+SPARSE_MODEL_NAME = "Qdrant/bm25"
+FASTEMBED_CACHE_PATH = "/models/fastembed"
+
+
+def render_message(message: Message) -> str:
+    parts_list: list[str] = []
+
+    if message.sender_id:
+        sender_name = message.sender_id.split("@")[0].replace(".", " ")
+        parts_list.append(f"[{sender_name}]:")
+
+    if message.text:
+        parts_list.append(message.text)
+
+    if message.parts:
+        for part in message.parts:
+            media_type = part.get("mediaType", "text")
+            part_text = part.get("text")
+            if isinstance(part_text, str) and part_text:
+                if media_type == "forward":
+                    parts_list.append(f"[Пересланное]: {part_text}")
+                elif media_type == "quote":
+                    parts_list.append(f"[Цитата]: {part_text}")
+                else:
+                    parts_list.append(part_text)
+
+    if message.file_snippets:
+        parts_list.append(f"[Файл]: {message.file_snippets}")
+
+    return " ".join(parts_list).strip()
+
+
+def build_chunks(
+    chat: Chat,
+    overlap_messages: list[Message],
+    new_messages: list[Message],
+) -> list[IndexAPIItem]:
+    new_messages = [m for m in new_messages if not m.is_system and not m.is_hidden]
+    overlap_messages = [m for m in overlap_messages if not m.is_system and not m.is_hidden]
+
+    result: list[IndexAPIItem] = []
+
+    def build_text_and_ranges(messages: list[Message]) -> tuple[str, list[tuple[int, int, str]]]:
+        text_parts: list[str] = []
+        message_ranges: list[tuple[int, int, str]] = []
+        position = 0
+
+        for index, message in enumerate(messages):
+            text = render_message(message)
+            if not text:
+                continue
+
+            if index > 0 and text_parts:
+                text_parts.append("\n")
+                position += 1
+
+            start = position
+            text_parts.append(text)
+            position += len(text)
+            message_ranges.append((start, position, message.id))
+
+        return "".join(text_parts), message_ranges
+
+    def slice_tail(text: str, tail_size: int) -> str:
+        if tail_size <= 0:
+            return ""
+        tail_start = max(0, len(text) - tail_size)
+        return text[tail_start:]
+
+    overlap_text, _ = build_text_and_ranges(overlap_messages)
+    previous_chunk_text = slice_tail(overlap_text, OVERLAP_SIZE)
+
+    new_text, new_message_ranges = build_text_and_ranges(new_messages)
+
+    for start in range(0, len(new_text), CHUNK_SIZE):
+        chunk_body = new_text[start: start + CHUNK_SIZE]
+        if not chunk_body:
+            continue
+
+        chunk_body_ranges = [
+            (
+                max(message_start, start) - start,
+                min(message_end, start + len(chunk_body)) - start,
+                message_id,
+            )
+            for message_start, message_end, message_id in new_message_ranges
+            if message_end > start and message_start < start + len(chunk_body)
+        ]
+
+        chunk_overlap = previous_chunk_text
+        chunk_text = chunk_overlap
+        if chunk_text and chunk_body:
+            chunk_text += "\n"
+        chunk_text += chunk_body
+
+        dense_text = f"[{chat.name}] {chunk_text}"
+        sparse_text = chunk_body
+
+        result.append(
+            IndexAPIItem(
+                page_content=chunk_text,
+                dense_content=dense_text,
+                sparse_content=sparse_text,
+                message_ids=[message_id for _, _, message_id in chunk_body_ranges],
+            )
+        )
+        previous_chunk_text = slice_tail(chunk_text, OVERLAP_SIZE)
+
+    return result
+
+
+app = FastAPI(title="Index Service", version="0.1.0")
 
 
 @app.get("/health")
@@ -30,16 +198,38 @@ async def health() -> dict[str, str]:
 async def index(payload: IndexAPIRequest) -> IndexAPIResponse:
     return IndexAPIResponse(
         results=build_chunks(
+            payload.data.chat,
             payload.data.overlap_messages,
             payload.data.new_messages,
         )
     )
 
 
+@lru_cache(maxsize=1)
+def get_sparse_model():
+    from fastembed import SparseTextEmbedding
+
+    logger.info("Loading sparse model %s from cache %s", SPARSE_MODEL_NAME, FASTEMBED_CACHE_PATH)
+    return SparseTextEmbedding(model_name=SPARSE_MODEL_NAME)
+
+
+def embed_sparse_texts(texts: list[str]) -> list[dict]:
+    model = get_sparse_model()
+    vectors = []
+    for item in model.embed(texts):
+        vectors.append(
+            {
+                "indices": item.indices.tolist(),
+                "values": item.values.tolist(),
+            }
+        )
+    return vectors
+
+
 @app.post("/sparse_embedding")
 async def sparse_embedding(payload: SparseEmbeddingRequest) -> dict[str, Any]:
     vectors = await asyncio.to_thread(embed_sparse_texts, payload.texts)
-    return {"vectors": [{"indices": v.indices, "values": v.values} for v in vectors]}
+    return {"vectors": vectors}
 
 
 @app.exception_handler(Exception)

index/rendering.py

@@ -2,7 +2,7 @@
 
 import datetime
 
-from .cleaning import CleanedMessage
+from cleaning import CleanedMessage
 
 
 def _format_time(ts: int) -> str:

index/sparse.py

@@ -2,7 +2,7 @@ import logging
 import os
 from functools import lru_cache
 
-from .schemas import SparseVector
+from index_schemas import SparseVector
 
 SPARSE_MODEL_NAME = "Qdrant/bm25"
 FASTEMBED_CACHE_PATH = "/models/fastembed"

kredit.md

@@ -0,0 +1,3 @@
+team_id: 35230
+vk login: 56aa86799bb9edc4
+vk password: edd89cea9ed0734d00ba6904cf7475d7

search/Makefile

@@ -1,6 +1,6 @@
 LOGIN ?=
 PASSWORD ?=
-TEAM_ID ?= 
+TEAM_ID ?= 35230
 DOCKER_REGISTRY_URL ?= 83.166.249.64:5000
 PORT ?= 8000
 QDRANT_URL ?=
@@ -16,16 +16,15 @@ REQUIRED_RUN_VARS := QDRANT_URL EMBEDDINGS_DENSE_URL API_KEY RERANKER_URL
 
 IMAGE = $(DOCKER_REGISTRY_URL)/$(TEAM_ID)/search-service:latest
 
-.PHONY: login build run push check-run-env
+.PHONY: login build run push release
 
 login:
-	@: $(if $(LOGIN),,$(error LOGIN is required for make login))
+	@: $(if $(LOGIN),,$(error LOGIN is required))
-	@: $(if $(PASSWORD),,$(error PASSWORD is required for make login))
+	@: $(if $(PASSWORD),,$(error PASSWORD is required))
 	docker login $(DOCKER_REGISTRY_URL) -u $(LOGIN) -p $(PASSWORD)
 
 build:
-	@: $(if $(TEAM_ID),,$(error TEAM_ID is required for make build))
+	docker build --platform linux/amd64 -t $(IMAGE) ./
-	docker build -t $(IMAGE) ./
 
 run: build
 	@: $(foreach var,$(REQUIRED_RUN_VARS),$(if $($(var)),,$(error $(var) is required for make run)))
@@ -41,5 +40,8 @@ run: build
 		-e QDRANT_SPARSE_VECTOR_NAME=$(QDRANT_SPARSE_VECTOR_NAME) \
 		$(IMAGE)
 
-push: login build
+push:
 	docker push $(IMAGE)
+
+release: build push
+	@echo "search-service pushed → $(IMAGE)"

search/aggregation.py

@@ -1,7 +1,7 @@
 from typing import Any
 
-from .config import TOP_K
+from config import TOP_K
-from .retrieval import extract_message_ids
+from retrieval import extract_message_ids
 
 
 def aggregate_message_ids(

search/config.py

@@ -19,10 +19,10 @@ QDRANT_SPARSE_VECTOR_NAME = os.getenv("QDRANT_SPARSE_VECTOR_NAME", "sparse")
 OPEN_API_LOGIN = os.getenv("OPEN_API_LOGIN")
 OPEN_API_PASSWORD = os.getenv("OPEN_API_PASSWORD")
 
-DENSE_PREFETCH_K = 50
+DENSE_PREFETCH_K = 80
-SPARSE_PREFETCH_K = 100
+SPARSE_PREFETCH_K = 200
-RETRIEVE_K = 80
+RETRIEVE_K = 150
-RERANK_LIMIT = 60
+RERANK_LIMIT = 15
 TOP_K = 50
 
 HTTP_TIMEOUT = 30.0

search/main.py

@@ -2,56 +2,157 @@ import asyncio
 import logging
 import os
 from contextlib import asynccontextmanager
+from functools import lru_cache
+from typing import Any
 
 import httpx
+from fastembed import SparseTextEmbedding
 from fastapi import FastAPI, HTTPException, Request
 from fastapi.exceptions import RequestValidationError
 from fastapi.responses import JSONResponse
-from qdrant_client import AsyncQdrantClient
+from pydantic import BaseModel, Field
+from qdrant_client import AsyncQdrantClient, models
 
-from .aggregation import aggregate_message_ids
+EMBEDDINGS_DENSE_MODEL = "Qwen/Qwen3-Embedding-0.6B"
-from .config import (
+
-    API_KEY,
+HOST = os.getenv("HOST", "0.0.0.0")
-    HOST,
+PORT = int(os.getenv("PORT", "8000"))
-    HTTP_MAX_RETRIES,
+
-    HTTP_TIMEOUT,
+API_KEY = os.getenv("API_KEY")
-    PORT,
+EMBEDDINGS_DENSE_URL = os.getenv("EMBEDDINGS_DENSE_URL")
-    QDRANT_URL,
+QDRANT_DENSE_VECTOR_NAME = os.getenv("QDRANT_DENSE_VECTOR_NAME", "dense")
-    logger,
+QDRANT_SPARSE_VECTOR_NAME = os.getenv("QDRANT_SPARSE_VECTOR_NAME", "sparse")
-    validate_required_env,
+SPARSE_MODEL_NAME = "Qdrant/bm25"
-)
+RERANKER_MODEL = "nvidia/llama-nemotron-rerank-1b-v2"
-from .query_builder import (
+RERANKER_URL = os.getenv("RERANKER_URL")
-    build_extra_dense_queries,
+OPEN_API_LOGIN = os.getenv("OPEN_API_LOGIN")
-    build_primary_query,
+OPEN_API_PASSWORD = os.getenv("OPEN_API_PASSWORD")
-    build_sparse_query,
+QDRANT_URL = os.getenv("QDRANT_URL")
-    embed_dense,
+QDRANT_COLLECTION_NAME = os.getenv("QDRANT_COLLECTION_NAME", "evaluation")
-    embed_dense_multi,
+REQUIRED_ENV_VARS = [
-    embed_sparse,
+    "EMBEDDINGS_DENSE_URL",
-)
+    "RERANKER_URL",
-from .rerank import rerank_points
+    "QDRANT_URL",
-from .retrieval import qdrant_search
+]
-from .schemas import SearchAPIItem, SearchAPIRequest, SearchAPIResponse, SparseVector
+
+logging.basicConfig(level=os.getenv("LOG_LEVEL", "INFO"))
+logger = logging.getLogger("search-service")
 
 
-async def _embed_dense_with_retry(client: httpx.AsyncClient, text: str) -> list[float]:
+def validate_required_env() -> None:
-    last_exc: Exception | None = None
+    if bool(OPEN_API_LOGIN) != bool(OPEN_API_PASSWORD):
-    for attempt in range(HTTP_MAX_RETRIES + 1):
+        raise RuntimeError("OPEN_API_LOGIN and OPEN_API_PASSWORD must be set together")
-        try:
+
-            return await embed_dense(client, text)
+    if not API_KEY and not (OPEN_API_LOGIN and OPEN_API_PASSWORD):
-        except (httpx.TransportError, httpx.HTTPStatusError) as exc:
+        raise RuntimeError("Either API_KEY or OPEN_API_LOGIN and OPEN_API_PASSWORD must be set")
-            if isinstance(exc, httpx.HTTPStatusError) and exc.response.status_code < 500:
+
-                raise
+    missing_env_vars = [
-            last_exc = exc
+        name for name in REQUIRED_ENV_VARS if os.getenv(name) is None or os.getenv(name) == ""
-            if attempt < HTTP_MAX_RETRIES:
+    ]
-                await asyncio.sleep(0.5 * (attempt + 1))
+    if not missing_env_vars:
-    raise RuntimeError(f"Dense embedding failed after retries: {last_exc}")
+        return
+
+    logger.error("Empty required env vars: %s", ", ".join(missing_env_vars))
+    raise RuntimeError(f"Empty required env vars: {', '.join(missing_env_vars)}")
+
+
+validate_required_env()
+
+
+def get_upstream_request_kwargs() -> dict[str, Any]:
+    headers = {"Content-Type": "application/json"}
+    kwargs: dict[str, Any] = {"headers": headers}
+
+    if OPEN_API_LOGIN and OPEN_API_PASSWORD:
+        kwargs["auth"] = (OPEN_API_LOGIN, OPEN_API_PASSWORD)
+        return kwargs
+
+    if API_KEY:
+        headers["Authorization"] = f"Bearer {API_KEY}"
+
+    return kwargs
+
+
+class DateRange(BaseModel):
+    from_: str = Field(alias="from")
+    to: str
+
+
+class Entities(BaseModel):
+    people: list[str] | None = None
+    emails: list[str] | None = None
+    documents: list[str] | None = None
+    names: list[str] | None = None
+    links: list[str] | None = None
+
+
+class Question(BaseModel):
+    text: str
+    asker: str = ""
+    asked_on: str = ""
+    variants: list[str] | None = None
+    hyde: list[str] | None = None
+    keywords: list[str] | None = None
+    entities: Entities | None = None
+    date_mentions: list[str] | None = None
+    date_range: DateRange | None = None
+    search_text: str = ""
+
+
+class SearchAPIRequest(BaseModel):
+    question: Question
+
+
+class SearchAPIItem(BaseModel):
+    message_ids: list[str]
+
+
+class SearchAPIResponse(BaseModel):
+    results: list[SearchAPIItem]
+
+
+class DenseEmbeddingItem(BaseModel):
+    index: int
+    embedding: list[float]
+
+
+class DenseEmbeddingResponse(BaseModel):
+    data: list[DenseEmbeddingItem]
+
+
+class SparseVector(BaseModel):
+    indices: list[int] = Field(default_factory=list)
+    values: list[float] = Field(default_factory=list)
+
+
+class ChunkMetadata(BaseModel):
+    chat_name: str
+    chat_type: str
+    chat_id: str
+    chat_sn: str
+    thread_sn: str | None = None
+    message_ids: list[str]
+    start: str
+    end: str
+    participants: list[str] = Field(default_factory=list)
+    mentions: list[str] = Field(default_factory=list)
+    contains_forward: bool = False
+    contains_quote: bool = False
+
+
+@lru_cache(maxsize=1)
+def get_sparse_model() -> SparseTextEmbedding:
+    logger.info("Loading local sparse model %s", SPARSE_MODEL_NAME)
+    return SparseTextEmbedding(model_name=SPARSE_MODEL_NAME)
 
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
-    validate_required_env()
+    app.state.http = httpx.AsyncClient()
-    app.state.http = httpx.AsyncClient(timeout=HTTP_TIMEOUT)
+    app.state.qdrant = AsyncQdrantClient(
-    app.state.qdrant = AsyncQdrantClient(url=QDRANT_URL, api_key=API_KEY)
+        url=QDRANT_URL,
+        api_key=API_KEY,
+    )
     try:
         yield
     finally:
@@ -59,7 +160,225 @@ async def lifespan(app: FastAPI):
         await app.state.qdrant.close()
 
 
-app = FastAPI(title="Search Service", version="0.2.0", lifespan=lifespan)
+app = FastAPI(title="Search Service", version="0.1.0", lifespan=lifespan)
+
+DENSE_PREFETCH_K = 120
+SPARSE_PREFETCH_K = 200
+RETRIEVE_K = 150
+RERANK_LIMIT = 35
+KEYWORD_BOOST_EXTRA = 10
+
+
+async def embed_dense(client: httpx.AsyncClient, text: str) -> list[float]:
+    response = await client.post(
+        EMBEDDINGS_DENSE_URL,
+        **get_upstream_request_kwargs(),
+        json={
+            "model": os.getenv("EMBEDDINGS_DENSE_MODEL", EMBEDDINGS_DENSE_MODEL),
+            "input": [text],
+        },
+    )
+    response.raise_for_status()
+    payload = DenseEmbeddingResponse.model_validate(response.json())
+    if not payload.data:
+        raise ValueError("Dense embedding response is empty")
+    return payload.data[0].embedding
+
+
+async def embed_dense_batch(client: httpx.AsyncClient, texts: list[str]) -> list[list[float]]:
+    response = await client.post(
+        EMBEDDINGS_DENSE_URL,
+        **get_upstream_request_kwargs(),
+        json={
+            "model": os.getenv("EMBEDDINGS_DENSE_MODEL", EMBEDDINGS_DENSE_MODEL),
+            "input": texts,
+        },
+    )
+    response.raise_for_status()
+    payload = DenseEmbeddingResponse.model_validate(response.json())
+    payload.data.sort(key=lambda x: x.index)
+    return [item.embedding for item in payload.data]
+
+
+def embed_sparse_sync(text: str) -> SparseVector:
+    vectors = list(get_sparse_model().embed([text]))
+    if not vectors:
+        raise ValueError("Sparse embedding response is empty")
+    item = vectors[0]
+    return SparseVector(
+        indices=[int(index) for index in item.indices.tolist()],
+        values=[float(value) for value in item.values.tolist()],
+    )
+
+
+def build_dense_query(question: Question) -> str:
+    q = question.search_text.strip() if question.search_text else question.text.strip()
+    return q
+
+
+def build_sparse_query(question: Question) -> str:
+    base = question.search_text.strip() if question.search_text else question.text.strip()
+    parts = [base]
+    if question.keywords:
+        parts.extend(question.keywords)
+    return " ".join(parts)
+
+
+def _build_keyword_set(question: Question) -> list[str]:
+    tokens: list[str] = []
+    if question.keywords:
+        tokens.extend(kw.lower() for kw in question.keywords if kw)
+    if question.entities:
+        for field in (
+            question.entities.people,
+            question.entities.emails,
+            question.entities.documents,
+            question.entities.names,
+            question.entities.links,
+        ):
+            tokens.extend(e.lower() for e in (field or []) if e)
+    return tokens
+
+
+def prefilter_for_rerank(
+    points: list[Any],
+    question: Question,
+) -> tuple[list[Any], list[Any]]:
+    """Select candidates for reranking: top by RRF + keyword-boosted stragglers."""
+    if not points:
+        return [], []
+
+    head = points[:RERANK_LIMIT]
+    tail = points[RERANK_LIMIT:]
+
+    keywords = _build_keyword_set(question)
+    if not keywords or not tail:
+        return head, tail
+
+    extra: list[Any] = []
+    remaining_tail: list[Any] = []
+    for p in tail:
+        if len(extra) >= KEYWORD_BOOST_EXTRA:
+            remaining_tail.append(p)
+            continue
+        content = ((p.payload or {}).get("page_content") or "").lower()
+        if any(kw in content for kw in keywords):
+            extra.append(p)
+        else:
+            remaining_tail.append(p)
+
+    return head + extra, remaining_tail
+
+
+async def qdrant_search(
+    client: AsyncQdrantClient,
+    dense_vectors: list[list[float]],
+    sparse_vector: SparseVector,
+) -> list[Any] | None:
+    prefetch_list = []
+    for dv in dense_vectors:
+        prefetch_list.append(
+            models.Prefetch(
+                query=dv,
+                using=QDRANT_DENSE_VECTOR_NAME,
+                limit=DENSE_PREFETCH_K,
+            )
+        )
+    prefetch_list.append(
+        models.Prefetch(
+            query=models.SparseVector(
+                indices=sparse_vector.indices,
+                values=sparse_vector.values,
+            ),
+            using=QDRANT_SPARSE_VECTOR_NAME,
+            limit=SPARSE_PREFETCH_K,
+        )
+    )
+
+    response = await client.query_points(
+        collection_name=QDRANT_COLLECTION_NAME,
+        prefetch=prefetch_list,
+        query=models.FusionQuery(fusion=models.Fusion.RRF),
+        limit=RETRIEVE_K,
+        with_payload=True,
+    )
+
+    if not response.points:
+        return None
+
+    return response.points
+
+
+def extract_message_ids(point: Any) -> list[str]:
+    payload = point.payload or {}
+    metadata = payload.get("metadata") or {}
+    message_ids = metadata.get("message_ids") or []
+    return [str(message_id) for message_id in message_ids]
+
+
+async def get_rerank_scores(
+    client: httpx.AsyncClient,
+    label: str,
+    targets: list[str],
+) -> list[float]:
+    if not targets:
+        return []
+
+    for attempt in range(5):
+        try:
+            response = await client.post(
+                RERANKER_URL,
+                **get_upstream_request_kwargs(),
+                json={
+                    "model": RERANKER_MODEL,
+                    "encoding_format": "float",
+                    "text_1": label,
+                    "text_2": targets,
+                },
+            )
+            if response.status_code == 429:
+                wait = 2 ** attempt
+                logger.warning(f"Rerank 429, retry {attempt+1}/5 in {wait}s")
+                await asyncio.sleep(wait)
+                continue
+            response.raise_for_status()
+            payload = response.json()
+            data = payload.get("data") or []
+            return [float(sample["score"]) for sample in data]
+        except Exception as e:
+            logger.warning(f"Rerank error attempt {attempt+1}/5: {e}")
+            if attempt < 4:
+                await asyncio.sleep(2 ** attempt)
+                continue
+            logger.error("Rerank failed after 5 attempts, using fallback")
+            return []
+
+    logger.error("Rerank 429 after 5 retries, using fallback")
+    return []
+
+
+async def rerank_points(
+    client: httpx.AsyncClient,
+    query: str,
+    points: list[Any],
+) -> list[Any]:
+    if not points:
+        return []
+    targets = [point.payload.get("page_content") for point in points]
+    scores = await get_rerank_scores(client, query, targets)
+
+    if not scores or len(scores) != len(points):
+        logger.warning("Reranker unavailable or score mismatch, returning RRF order")
+        return points
+
+    return [
+        point
+        for _, point in sorted(
+            zip(scores, points),
+            key=lambda item: item[0],
+            reverse=True,
+        )
+    ]
 
 
 @app.get("/health")
@@ -70,39 +389,62 @@ async def health() -> dict[str, str]:
 @app.post("/search", response_model=SearchAPIResponse)
 async def search(payload: SearchAPIRequest) -> SearchAPIResponse:
     question = payload.question
-    primary_query = build_primary_query(question)
+    query = question.text.strip()
-    if not primary_query:
+    if not query:
         raise HTTPException(status_code=400, detail="question.text is required")
 
     client: httpx.AsyncClient = app.state.http
     qdrant: AsyncQdrantClient = app.state.qdrant
 
-    extra_texts = build_extra_dense_queries(question)
+    dense_query = build_dense_query(question)
-    sparse_text = build_sparse_query(question)
+    sparse_query = build_sparse_query(question)
 
-    async def _no_extra() -> list:
+    dense_task = embed_dense(client, dense_query)
-        return []
+    sparse_task = asyncio.to_thread(lambda: embed_sparse_sync(sparse_query))
+    dense_vector, sparse_vector = await asyncio.gather(dense_task, sparse_task)
 
-    extra_task = embed_dense_multi(client, extra_texts) if extra_texts else _no_extra()
+    dense_vectors = [dense_vector]
-    primary_dense, extra_dense_vecs, sparse_vec = await asyncio.gather(
+    extra_texts: list[str] = []
-        _embed_dense_with_retry(client, primary_query),
+    raw_text = question.text.strip()
-        extra_task,
+    if raw_text and raw_text != dense_query:
-        asyncio.to_thread(embed_sparse, sparse_text),
+        extra_texts.append(raw_text)
-    )
+    for v in (question.variants or []):
+        q_v = v.strip()
+        if q_v and q_v != dense_query and q_v not in extra_texts:
+            extra_texts.append(q_v)
+    for h in (question.hyde or []):
+        q_h = h.strip()
+        if q_h and q_h != dense_query and q_h not in extra_texts:
+            extra_texts.append(q_h)
+    extra_texts = extra_texts[:3]
+    if extra_texts:
+        try:
+            extra_vecs = await embed_dense_batch(client, extra_texts)
+            dense_vectors.extend(extra_vecs)
+        except Exception as e:
+            logger.warning(f"Extra dense embedding failed: {e}")
 
-    points = await qdrant_search(
+    all_points = await qdrant_search(qdrant, dense_vectors, sparse_vector)
-        qdrant,
-        primary_dense,
-        extra_dense_vecs,
-        sparse_vec,
-        question,
-    )
 
-    if not points:
+    if all_points is None:
         return SearchAPIResponse(results=[])
 
-    reranked_head, retrieval_tail = await rerank_points(client, primary_query, points)
+    all_points = list(all_points)
-    message_ids = aggregate_message_ids(reranked_head, retrieval_tail)
+
+    rerank_pool, rerank_tail = prefilter_for_rerank(all_points, question)
+    reranked = await rerank_points(client, query, rerank_pool)
+    final_points = reranked + rerank_tail
+
+    msg_score: dict[str, float] = {}
+    for rank, point in enumerate(reranked):
+        score = 1.0 / (rank + 1)
+        for mid in extract_message_ids(point):
+            msg_score[mid] = msg_score.get(mid, 0.0) + score
+    for rank, point in enumerate(rerank_tail):
+        score = 1.0 / (60 + rank + 1)
+        for mid in extract_message_ids(point):
+            msg_score[mid] = msg_score.get(mid, 0.0) + score
+    message_ids = sorted(msg_score, key=lambda m: msg_score[m], reverse=True)[:50]
 
     return SearchAPIResponse(results=[SearchAPIItem(message_ids=message_ids)])
 

search/query_builder.py

@@ -1,4 +1,3 @@
-import asyncio
 import os
 import re
 from functools import lru_cache
@@ -6,14 +5,14 @@ from functools import lru_cache
 import httpx
 from fastembed import SparseTextEmbedding
 
-from .config import (
+from config import (
     EMBEDDINGS_DENSE_MODEL,
     EMBEDDINGS_DENSE_URL,
     SPARSE_MODEL_NAME,
     get_upstream_kwargs,
     logger,
 )
-from .schemas import DenseEmbeddingResponse, Question, SparseVector
+from schemas import DenseEmbeddingResponse, Question, SparseVector
 
 
 @lru_cache(maxsize=1)
@@ -38,9 +37,20 @@ async def embed_dense(client: httpx.AsyncClient, text: str) -> list[float]:
     return payload.data[0].embedding
 
 
-async def embed_dense_multi(client: httpx.AsyncClient, texts: list[str]) -> list[list[float]]:
+async def embed_dense_batch(client: httpx.AsyncClient, texts: list[str]) -> list[list[float]]:
-    tasks = [embed_dense(client, t) for t in texts]
+    """Single request for multiple texts — avoids N parallel calls and rate limiting."""
-    return list(await asyncio.gather(*tasks))
+    response = await client.post(
+        str(EMBEDDINGS_DENSE_URL),
+        **get_upstream_kwargs(),
+        json={
+            "model": os.getenv("EMBEDDINGS_DENSE_MODEL", EMBEDDINGS_DENSE_MODEL),
+            "input": texts,
+        },
+    )
+    response.raise_for_status()
+    payload = DenseEmbeddingResponse.model_validate(response.json())
+    payload.data.sort(key=lambda x: x.index)
+    return [item.embedding for item in payload.data]
 
 
 def embed_sparse(text: str) -> SparseVector:

search/rerank.py

@@ -1,12 +1,13 @@
+import asyncio
 from typing import Any
 
 import httpx
 
-from .config import RERANK_LIMIT, RERANKER_MODEL, RERANKER_URL, get_upstream_kwargs, logger
+from config import RERANK_LIMIT, RERANKER_MODEL, RERANKER_URL, get_upstream_kwargs, logger
-from .retrieval import extract_page_content
+from retrieval import extract_page_content
 
 
-async def get_rerank_scores(
+async def _get_rerank_scores(
     client: httpx.AsyncClient,
     query: str,
     targets: list[str],
@@ -14,20 +15,36 @@ async def get_rerank_scores(
     if not targets:
         return []
 
-    response = await client.post(
+    for attempt in range(5):
-        str(RERANKER_URL),
+        try:
-        **get_upstream_kwargs(),
+            response = await client.post(
-        json={
+                str(RERANKER_URL),
-            "model": RERANKER_MODEL,
+                **get_upstream_kwargs(),
-            "encoding_format": "float",
+                json={
-            "text_1": query,
+                    "model": RERANKER_MODEL,
-            "text_2": targets,
+                    "encoding_format": "float",
-        },
+                    "text_1": query,
-    )
+                    "text_2": targets,
-    response.raise_for_status()
+                },
+            )
+        except Exception as exc:
+            if attempt < 4:
+                await asyncio.sleep(2 ** attempt)
+                continue
+            raise exc
 
-    data = response.json().get("data") or []
+        if response.status_code == 429:
-    return [float(sample["score"]) for sample in data]
+            wait = 2 ** attempt
+            logger.warning("Rerank 429, retry %d/5 in %ds", attempt + 1, wait)
+            await asyncio.sleep(wait)
+            continue
+
+        response.raise_for_status()
+        data = response.json().get("data") or []
+        return [float(sample["score"]) for sample in data]
+
+    logger.error("Rerank 429 after all retries, falling back")
+    return []
 
 
 async def rerank_points(
@@ -35,24 +52,22 @@ async def rerank_points(
     query: str,
     points: list[Any],
 ) -> tuple[list[Any], list[Any]]:
-    """Return (reranked_head, retrieval_tail) so we don't lose candidates."""
     if not points:
         return [], []
 
-    rerank_candidates = points[:RERANK_LIMIT]
+    head = points[:RERANK_LIMIT]
     tail = points[RERANK_LIMIT:]
+    targets = [extract_page_content(p) for p in head]
 
-    targets = [extract_page_content(p) for p in rerank_candidates]
     try:
-        scores = await get_rerank_scores(client, query, targets)
+        scores = await _get_rerank_scores(client, query, targets)
     except Exception as exc:
         logger.warning("Rerank failed, using retrieval order: %s", exc)
-        return rerank_candidates, tail
+        return head, tail
 
-    if len(scores) != len(rerank_candidates):
+    if len(scores) != len(head):
         logger.warning("Rerank score count mismatch, using retrieval order")
-        return rerank_candidates, tail
+        return head, tail
 
-    paired = sorted(zip(scores, rerank_candidates), key=lambda x: x[0], reverse=True)
+    reranked = [p for _, p in sorted(zip(scores, head), key=lambda x: x[0], reverse=True)]
-    reranked = [p for _, p in paired]
     return reranked, tail

search/retrieval.py

@@ -2,7 +2,7 @@ from typing import Any
 
 from qdrant_client import AsyncQdrantClient, models
 
-from .config import (
+from config import (
     DENSE_PREFETCH_K,
     QDRANT_COLLECTION_NAME,
     QDRANT_DENSE_VECTOR_NAME,
@@ -11,22 +11,28 @@ from .config import (
     SPARSE_PREFETCH_K,
     logger,
 )
-from .schemas import Question, SparseVector
+from schemas import Question, SparseVector
 
 
 def _build_filter(question: Question) -> models.Filter | None:
     must_conditions: list[models.Condition] = []
 
     if question.date_range:
-        must_conditions.append(
+        try:
-            models.FieldCondition(
+            must_conditions.append(
-                key="metadata.start",
+                models.FieldCondition(
-                range=models.Range(
+                    key="metadata.end",
-                    gte=question.date_range.from_,
+                    range=models.Range(gte=question.date_range.from_),
-                    lte=question.date_range.to,
+                )
-                ),
             )
-        )
+            must_conditions.append(
+                models.FieldCondition(
+                    key="metadata.start",
+                    range=models.Range(lte=question.date_range.to),
+                )
+            )
+        except Exception as e:
+            logger.warning("Date filter failed: %s", e)
 
     if question.asker:
         must_conditions.append(

tests/test_aggregation.py

@@ -1,15 +1,17 @@
 """Unit tests for search/aggregation.py"""
 import sys
 import os
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+_SEARCH_DIR = os.path.join(os.path.dirname(__file__), "..", "search")
+sys.path.insert(0, _SEARCH_DIR)
 
 os.environ.setdefault("EMBEDDINGS_DENSE_URL", "http://localhost/embed")
 os.environ.setdefault("RERANKER_URL", "http://localhost/rerank")
 os.environ.setdefault("QDRANT_URL", "http://localhost:6333")
 os.environ.setdefault("API_KEY", "test-key")
 
-from search.aggregation import aggregate_message_ids
+from aggregation import aggregate_message_ids
-from search.config import TOP_K
+from config import TOP_K
 
 
 def _point(message_ids: list[str]):

tests/test_chunking.py

@@ -1,11 +1,13 @@
 """Unit tests for index/chunking.py"""
 import sys
 import os
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
 
-from index.chunking import build_chunks, _split_windows, WINDOW_MAX_MESSAGES, TIME_GAP_SECONDS
+_INDEX_DIR = os.path.join(os.path.dirname(__file__), "..", "index")
-from index.cleaning import CleanedMessage
+sys.path.insert(0, _INDEX_DIR)
-from index.schemas import Message
+
+from chunking import build_chunks, _split_windows, WINDOW_MAX_MESSAGES, TIME_GAP_SECONDS
+from cleaning import CleanedMessage
+from index_schemas import Message
 
 
 def _make_message(id: str, time: int, text: str = "hello", **kwargs) -> Message:

tests/test_cleaning.py

@@ -1,17 +1,19 @@
 """Unit tests for index/cleaning.py"""
 import sys
 import os
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+_INDEX_DIR = os.path.join(os.path.dirname(__file__), "..", "index")
+sys.path.insert(0, _INDEX_DIR)
 
 import pytest
-from index.cleaning import (
+from cleaning import (
     normalize_unicode,
     parse_file_snippets,
     normalize_member_event,
     normalize_part,
     clean_message,
 )
-from index.schemas import Message
+from index_schemas import Message
 
 
 def _make_message(**kwargs) -> Message:

tests/test_query_builder.py

@@ -1,7 +1,9 @@
 """Unit tests for search/query_builder.py (pure logic only, no HTTP)"""
 import sys
 import os
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+_SEARCH_DIR = os.path.join(os.path.dirname(__file__), "..", "search")
+sys.path.insert(0, _SEARCH_DIR)
 
 # Stub env vars before importing search modules
 os.environ.setdefault("EMBEDDINGS_DENSE_URL", "http://localhost/embed")
@@ -9,8 +11,8 @@ os.environ.setdefault("RERANKER_URL", "http://localhost/rerank")
 os.environ.setdefault("QDRANT_URL", "http://localhost:6333")
 os.environ.setdefault("API_KEY", "test-key")
 
-from search.schemas import Entities, Question
+from schemas import Entities, Question
-from search.query_builder import (
+from query_builder import (
     build_primary_query,
     build_extra_dense_queries,
     build_sparse_query,

tests/test_rendering.py

@@ -1,10 +1,12 @@
 """Unit tests for index/rendering.py"""
 import sys
 import os
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
 
-from index.cleaning import CleanedMessage
+_INDEX_DIR = os.path.join(os.path.dirname(__file__), "..", "index")
-from index.rendering import render_page_content, render_dense_content, render_sparse_content
+sys.path.insert(0, _INDEX_DIR)
+
+from cleaning import CleanedMessage
+from rendering import render_page_content, render_dense_content, render_sparse_content
 
 
 def _make_cleaned(**kwargs) -> CleanedMessage: